Agris on-line Papers in Economics and Informatics

Faculty of Economics and Management CULS Prague, Kamýcká 129, 165 00 Praha - Suchdol

The international peer-reviewed scientific journal, ISSN 1804-1930

Do Sunspots Matter for Cycles in Agricultural Lending: a VEC Approach to Russian Wheat Market

D. Burakov
DOI: 10.7160/aol.2017.090102
Agris on-line Papers in Economics and Informatics, no 1/2017, March

Burakov, D. (2017) “Do Sunspots Matter for Cycles in Agricultural Lending: a VEC Approach to Russian Wheat Market", AGRIS on-line Papers in Economics and Informatics, Vol. 9, No. 1, pp. 17 - 31. ISSN 1804-1930.DOI 10.7160/aol.2017.090102.

In this article, we test a hypothesis about the influence of sunspot cycles on cycles of agricultural lending on example of wheat market. Analyzing data on Russian wheat market for period from 1990 to 2015 we test a hypothesis of solar activity’s impact on cycles in agricultural lending in the short and long run. Using a vector error correction approach to the sample, we obtain the following results: in the long run, sunspots, wheat yield, world wheat prices, and non-performing loans (NPL) for wheat market are related. In the short run, level of non-performing wheat loans depends only on wheat yields. However, results of Granger causality test confirm that wheat yield dynamics and sunspots Granger cause non-performing bank loans in Russia, which confirms our hypothesis of solar activity importance for agricultural lending activity.

Solar activity, wheat market, agricultural lending, crop yield, vector error correction, Granger causality test, credit cycle.


  1. Australian Exports Grains Innovation Centrer (AEGIC) (2016). "Russia’s wheat industry: implications for Australia". [Online]. Available: http:/ Russia-wheat-industry-Implications-for-Australia.pdf [Accessed:10 Dec. 2016].
  2. Bank of Russia Statistical Bulletin (1990-2015). [Online]. Available: [Accessed: 23 Dec. 2016].
  3. Basso, B., Cammarano, D. and Carfagna, E. (2013) "Review of crop yield forecasting methods and early warning systems", In Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome Italy, 18–19 July 2013. [Online]. Available: http:/ templates/ess/documents/meetings_and_workshops/GS_SAC_2013/Improving_methods_for_ crops_estimates/Crop_Yield_Forecasting_Methods_and_Early_Warning_Systems_Lit_review. pdf+&cd=1&hl=ru&ct=clnk&gl=ru [Accessed: 18 Dec. 2016].
  4. Dewey, E. (1968) “Economic and Sociological Phenomena Related to Solar Activity and Influence”, Cycles Magazine, Vol. 19, pp. 201-207. ISSN 0011-4294.
  5. Dickey, D. and Fuller, W. (1979) “Distribution of the estimators for autoregressive time series with a unit root”, Journal of American Statistical Association, Vol. 74, pp. 427-431. ISSN 0162-1459. DOI 10.2307/2286348.
  6. FAOSTAT: Food and Agriculture Organization of the United Nations [DB/OL] (2016) [Online]. Available: http:/ [Accessed 5 Dec. 2016].
  7. Far Eastern Department for Hydrometeorology and Environmental Monitoring of Russia (2016) [Online]. Available: http:/ [Accessed: 18 Dec. 2016].
  8. Federal State Statistics Service of Russia (2016) [Online]. Available: http:/ [Accessed: 19 Dec. 2016].
  9. Garnett, R., Nirupama, N., Haque, C. and Murty, T. S. (2006). “Correlates of Canadian Prairie summer rainfall: implications for crop yields”, Climate Research, Vol. 32, pp. 25-33. ISSN 1616-1572. DOI 10.3354/cr032025.
  10. Gorbanev, M. (2012) “Sunspots, unemployment, and recessions, or Can the solar activity cycle shape the business cycle?”, MPRA Paper 40271, University Library of Munich, Germany. [Online]. Available: [Accessed: 28 Nov. 2016].
  11. Gujarati, D. (2008) "Basic Econometrics", 4th Edition, Mc Graw-Hill, London. p. 944. ISBN 978-0073375779.
  12. Gupta, R., Gil-Alana, L. and Yaya, O. (2015) „Do sunspot numbers cause global temperatures? Evidence from a frequency domain causality test“, Applied Economics, Vol. 47, No. 8, pp. 798-808. ISSN 1466-4283. DOI 10.1080/00036846.2014.980575.
  13. Granger, C. W. J. (1969) „Investigating Causal Relations by Econometric Models and Cross-spectral Methods“, Econometrica, Vol. 37, No. 3, pp. 424–438. ISSN 1468-0262. DOI 10.2307/1912791.
  14. Granger, C. W. J. (1988) "Some Recent Development in a Concept of Causality", Journal of Econometrics, Vol. 39, pp. 199-211. ISSN 0304-4076. DOI 10.1016/0304-4076(88)90045-0.
  15. Harrison, V. L. (1976) “Do Sunspot Cycles Affect Crop Yields?”, Agriculture Economic Report, Vol. 327, pp. 1–23.
  16. Herschel, W. (1801) „Observations tending to investigate the nature of the Sun, in order to find the causes or symptoms of its variable emission of light and heat; with remarks on the use that may possibly be drawn from solar observations“, Philosophical Transactions of the Royal Society of London, Vol. 91, pp. 265–318.
  17. Hiremath, K. M. (2006) “The Influence of Solar Activity on the Rainfall over India: Cycle-to- Cycle Variations”, Journal of Astrophysics and Astronomy, Vol. 27, pp. 367–372. ISSN 0973-7758. DOI 10.1007/BF02702543.
  18. Hiremath, K. M. and Mandi, P. (2004) “Influence of the solar activity on the Indian Monsoon rainfall”, New Astronomy, Vol. 9, pp. 651-662. ISSN 1384-1076. DOI 10.1016/j.newast.2004.04.001.
  19. Hirshleifer, D. and Shumway, T. (2003) „Good day sunshine: Stock returns and the weather.“ Journal of Finance, Vol. 58, No. 3, pp. 1009-1032. ISSN 1540-6261. DOI 10.1111/1540-6261.00556.
  20. Huhtamaa, H., Helama, S., Holopainen, S., Rethorn, C. and Rohr, C. (2015) “Crop yield responses to temperature fluctuations in 19th century Finland: provincial variation in relation to climate and tree-rings”, Boreal Environment Research, Vol.20, pp. 707-722. ISSN 1239-6095.
  21. International Monetary Fund (2014) "Quarterly National Accounts Manual: Concepts, Data Sources and Compilation". [Online]. Available: http:/ Textbook [Accessed: 16 Nov. 2016].
  22. Jevons, W. S. (1879) „Sunspots and commercial crises“, Nature, Vol.19, pp. 588–590. ISSN 0028-0836.
  23. Johansen, S. and Juselius, K. (1990) „Maximum likehood estimation and inference on co-integration with applications to the demand for money“, Oxford Bulletin of Economics and Statistics, Vol. 52, pp. 169-210. ISSN 1468-0084. DOI 10.1111/j.1468-0084.1990.mp52002003.x.
  24. Johansen, S. (1991) „Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models“, Econometrica, Vol. 59, No. 6, pp.1551–1580. ISSN 1468-0262. DOI 10.2307/2938278.
  25. Kamstra, M., Kramer, L. and Levi, M. (2003) "Winter Blues: A SAD Stock Market Cycle", American Economic Review, Vol. 93, No. 1, pp. 324-343. ISSN 0002-8282. DOI 10.1257/000282803321455322.
  26. Lockwood, M. (2012) “Solar Influence on Global and Regional Climates”, Surveys in Geophysics, Vol. 33, pp. 503-534. ISSN 0169-3298. DOI 10.1007/s10712-012-9181-3.
  27. Love, J. (2013) „On the insignificance of Herschel’s sunspot correlation“, Geophysical Research Letters, Vol. 40, pp. 4171-4176. ISSN 1944-8007. DOI 10.1002/grl.50846.
  28. Maddala, G. S. and Kim, In-Moo (1998) „Unit Roots, Cointegration, and Structural Change“, Cambridge: Cambridge University Press. pp. 364–365. ISBN 0-521-58782-4.
  29. Marsh, N. and Svensmark, H. (2000) “Cosmic rays, clouds, and climate”, Space Science Reviews, Vol. 94, pp. 215–230. ISSN 1572-9672. DOI 10.1051/epn/2015204.
  30. Monteith, J. (1972) “Solar Radiation and Productivity in Tropical Ecosystems”, Journal of Applied Ecology, Vol. 9, No. 3, pp. 747-766. ISSN 1365-2664. DOI 10.2307/2401901.
  31. Novy-Marx, R. (2014) “Predicting anomaly performance with politics, the weather, global warming, sunspots, and the stars”, Journal of Financial Economics, Vol. 112, No. 2, pp. 137-146. ISSN 0304-405X. DOI 10.1016/j.jfineco.2014.02.002.
  32. Otsu, A., Chinami, M., Morgenthale, S., Kaneko, Y., Fujita, D. and Shirakawa, T. (2006) “Correlations For number of sunspots, unemployment rate, and suicide mortality in Japan”, Perceptual and Motor Skills, Vol.102, No. 2, pp. 603-608. ISSN 0031-5125. DOI 10.2466/pms.102.2.603-608.
  33. Phillips, P. C. B. and Perron, P. (1988) “Testing for Unit Root in Time Series Regression”, Biometrica, Vol. 5, pp. 335-346. ISSN 0006-3444. DOI 10.1093/biomet/75.2.335.
  34. Pustil’nik, L. A. and Yom Din, G. (2004) “Space climate manifestations in Earth prices – from medieval England up to modern U.S.A.”, Solar Physics, Vol. 224, pp. 473 - 481. ISSN 0038-0938. DOI 10.1007/s11207-005-5198-9.
  35. Pustil’nik, L. A. and Yom Din, G. (2013) “On possible influence of space weather on agricultural markets: Necessary conditions and probable scenarios”, Astrophysics Bulletin, Vol. 68, pp. 107–124. ISSN 1990-3421. DOI 10.1134/S1990341313010100.
  36. Sadanandan, A. (2014) „Political Economy of Suicide: Financial Reforms, Credit Crunches and Farmer Suicides in India“, The Journal of Developing Areas, Vol. 48, No. 4, pp. 287-307. ISSN 0022-037X. DOI 10.1353/jda.2014.0065.
  37. Salin, V., Mwanamambo, B. and Mukumbuta, L. (2008) „Lending to Agribusinesses in Zambia“, [Online]. Available:http:/ lending_to_agribusiness_zambia.pdf+&cd=1&hl=ru&ct=clnk&gl=ru [Accessed:15 Nov. 2016].
  38. Saunders, E. M. (1993) “Stock prices and wall street weather“, American Economic Review, Vol. 83, No. 5, pp. 1337-1345. ISSN 0002-8282.
  39. Savin, I. and Leo, O. (2016) “Solar-Caused Fluctuations in Earth’s Magnetic Field and Statistical Wheat (Triticum, L., 1753) Yield”, Agrobiology, Vol. 51, No. 3, pp. 351-359. ISSN 1804-2686. DOI 10.15389/agrobiology.2016.3.351eng.
  40. Walsh, B. (1993) “Economic Cycles and Changes in the Earth's Geomagnetic Field”, Cycles Magazine, Vol. 44, pp. 76-80. ISSN 0011-4294.
Full paper

icon of PDF dokuments Full paper (.pdf, 499.64 KB ).

ISSN 1804-1930
© Agris on-line Papers in Economics and Informatics, 2009 - 2017
Faculty of Economics and Management CULS Prague, Kamycka 129, 165 00 Praha 6 - Suchdol